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Objective: Chronic hepatitis B (CHB) virus infection remains a major public health concern. In this study, the diagnostic capability of C-X-C chemokine receptor type 4 promoter methylation in patients with CHB-associated liver fibrosis/cirrhosis was evaluated., Methods: Two hundred participants were recruited, including 25 healthy controls (HCs), 60 patients with CHB and 115 patients with hepatitis B virus (HBV)-related liver fibrosis/LC. Researchers monitored the methylation and messenger ribonucleic acid (mRNA) levels of C-X-C chemokine receptor type 4 (CXCR4) in peripheral blood mononuclear cells (PBMCs). In addition, we utilized single cell sequencing to analyze the cell types highly expressing CXCR4 in HBV-related liver fibrosis/LC., Results: HBV-related fibrosis/cirrhosis patients exhibited a significant elevation in the expression level of CXCR4 mRNA in PBMCs compared to CHB ones. The CXCR4 promoter showed a significantly lower methylation level in patients with CHB-related fibrosis/cirrhosis than in patients with CHB. Additionally, the diagnostic area under the area under the curve (AUC) of methylation of the CXCR4 promoter for CHB -related liver fibrosis/LC exceeded liver stiffness measurement (LSM), aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 score (FIB-4). Furthermore, single-cell analysis demonstrated that CXCR4 expression is closely associated with Natural Killer cells(NK cells), T lymphocytes (T cells), and monocytes., Conclusion: The low methylation of the CXCR4 promoter holds promise as a non-invasive biomarker for detecting CHB-associated liver fibrosis/LC., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

The type V-I CRISPR-Cas system is becoming increasingly more attractive for genome editing. However, natural nucleases of this system often exhibit low efficiency, limiting their application. Here, we used structure-guided rational design and protein engineering to optimize an uncharacterized Cas12i nuclease, Cas12i3. As a result, we developed Cas-SF01, a Cas12i3 variant that exhibits significantly improved gene editing activity in mammalian cells. Cas-SF01 shows comparable or superior editing performance compared to SpCas9 and other Cas12 nucleases. Compared to natural Cas12i3, Cas-SF01 has an expanded PAM range and effectively recognizes NTTN and noncanonical NATN and TTVN PAMs. In addition, we identified an amino acid substitution, D876R, that markedly reduced the off-target effect while maintaining high on-target activity, leading to the development of Cas-SF01 HiFi (high-fidelity Cas-SF01). Finally, we show that Cas-SF01 has high gene editing activities in mice and plants. Our results suggest that Cas-SF01 can serve as a robust gene editing platform with high efficiency and specificity for genome editing applications in various organisms., Competing Interests: The authors declare no competing interests., (© 2024 The Author(s).)

Motivation: Drug-drug interaction (DDI) prediction is a challenging problem in pharmacology and clinical applications. With the increasing availability of large biomedical databases, large-scale biological knowledge graphs containing drug information have been widely used for DDI prediction. However, large knowledge graphs inevitably suffer from data noise problems, which limit the performance and interpretability of models based on the knowledge graph. Recent studies attempt to improve models by introducing inductive bias through an attention mechanism. However, they all only depend on the topology of entity nodes independently to generate fixed attention pathways, without considering the semantic diversity of entity nodes in different drug pair links. This makes it difficult for models to select more meaningful nodes to overcome data quality limitations and make more interpretable predictions., Results: To address this issue, we propose a Link-aware Graph Attention method for DDI prediction, called LaGAT, which is able to generate different attention pathways for drug entities based on different drug pair links. For a drug pair link, the LaGAT uses the embedding representation of one of the drugs as a query vector to calculate the attention weights, thereby selecting the appropriate topological neighbor nodes to obtain the semantic information of the other drug. We separately conduct experiments on binary and multi-class classification and visualize the attention pathways generated by the model. The results prove that LaGAT can better capture semantic relationships and achieves remarkably superior performance over both the classical and state-of-the-art models on DDI prediction., Availabilityand Implementation: The source code and data are available at https://github.com/Azra3lzz/LaGAT., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Recently developed base editors provide a powerful tool for plant research and crop improvement. Although a number of different deaminases and Cas proteins have been used to improve base editors the editing efficiency, and editing window are still not optimal. Fusion of a non-sequence-specific single-stranded DNA-binding domain (DBD) from the human Rad51 protein between Cas9 nickase and the deaminase has been reported to dramatically increase the editing efficiency and expand the editing window of base editors in the mammalian cell lines and mouse embryos. We report the use of this strategy in rice, by fusing a rice codon-optimized human Rad51 DBD to the cytidine base editors AncBE4max, AncBE4max-NG, and evoFERNY. Our results show that the addition of Rad51 DBD did not increase editing efficiency in the major editing window but the editing range was expanded in all the three systems. Replacing the human Rad51 DBD with the rice Rad51 DBD homolog also expanded the editing window effectively., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wei, Liu, Wang, Luo, Chen, Li, Wang, Botella and Zhang.)

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purpose is to facilitate the reusability and accessibility of the rapidly growing human metagenomic data. This is achieved by consistently annotating the microbial contents of collected samples using state-of-art toolsets and by manual curation of the meta-data of the corresponding human hosts. GMrepo organizes the collected samples according to their associated phenotypes and includes all possible related meta-data such as age, sex, country, body-mass-index (BMI) and recent antibiotics usage. To make relevant information easier to access, GMrepo is equipped with a graphical query builder, enabling users to make customized, complex and biologically relevant queries. For example, to find (1) samples from healthy individuals of 18 to 25 years old with BMIs between 18.5 and 24.9, or (2) projects that are related to colorectal neoplasms, with each containing >100 samples and both patients and healthy controls. Precomputed species/genus relative abundances, prevalence within and across phenotypes, and pairwise co-occurrence information are all available at the website and accessible through programmable interfaces. So far, GMrepo contains 58 903 human gut samples/runs (including 17 618 metagenomes and 41 285 amplicons) from 253 projects concerning 92 phenotypes. GMrepo is freely available at: https://gmrepo.humangut.info., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)